Classifying drivers using electronic logging devices

In the era of personalization, being able to determine the risk of individual drivers and hence provide suitable insurance coverage to them would be a logical step. This paper proposes risk scoring for motor insurance using logged data of the drivers that are collected electronically. The proposed method uses machine learning to create a model that can be applied using the logged data. Initial studies conducted were able to achieve up to an accuracy of 79.4%. With further improvement, it can provide a suitable individual risk scoring for insurance premium computation.